Introduction to Area Unit Data

Author

Serge Rey

Published

February 21, 2023

Areal Unit Data

Code

import geopandas
import libpysal

Code

south = libpysal.examples.load_example('South')

Code

libpysal.examples.explain('South')

Loading

Code

south_gdf = geopandas.read_file(south.get_path('south.shp'))

Plotting Geometries

Code

south_gdf.plot()

Checking CRS

Code

south_gdf.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

Turning off axis

Code

ax = south_gdf.plot()
ax.set_axis_off();

Inspecting the GeoDataFrame

Code

south_gdf.shape

(1412, 70)

Code

south_gdf.geometry

0       POLYGON ((-80.62805 40.39816, -80.60204 40.480...
1       POLYGON ((-80.52625 40.16245, -80.5876 40.1750...
2       POLYGON ((-80.52517 40.02275, -80.73843 40.035...
3       POLYGON ((-80.52447 39.72113, -80.83248 39.718...
4       POLYGON ((-75.7727 39.38301, -75.79144 39.7237...
                              ...                        
1407    POLYGON ((-79.14433 36.54606, -79.21706 36.549...
1408    POLYGON ((-79.43775 37.61596, -79.45834 37.603...
1409    POLYGON ((-80.12475 37.1251, -80.14045 37.1283...
1410    POLYGON ((-76.39569 37.10771, -76.4027 37.0905...
1411    POLYGON ((-77.53178 38.56506, -77.72094 38.840...
Name: geometry, Length: 1412, dtype: geometry

Code

south_gdf.columns

Index(['NAME', 'STATE_NAME', 'STATE_FIPS', 'CNTY_FIPS', 'FIPS', 'STFIPS',
       'COFIPS', 'FIPSNO', 'SOUTH', 'HR60', 'HR70', 'HR80', 'HR90', 'HC60',
       'HC70', 'HC80', 'HC90', 'PO60', 'PO70', 'PO80', 'PO90', 'RD60', 'RD70',
       'RD80', 'RD90', 'PS60', 'PS70', 'PS80', 'PS90', 'UE60', 'UE70', 'UE80',
       'UE90', 'DV60', 'DV70', 'DV80', 'DV90', 'MA60', 'MA70', 'MA80', 'MA90',
       'POL60', 'POL70', 'POL80', 'POL90', 'DNL60', 'DNL70', 'DNL80', 'DNL90',
       'MFIL59', 'MFIL69', 'MFIL79', 'MFIL89', 'FP59', 'FP69', 'FP79', 'FP89',
       'BLK60', 'BLK70', 'BLK80', 'BLK90', 'GI59', 'GI69', 'GI79', 'GI89',
       'FH60', 'FH70', 'FH80', 'FH90', 'geometry'],
      dtype='object')

Code

south_gdf.explore(column='HR60')

Make this Notebook Trusted to load map: File -> Trust Notebook

Code

south_gdf.HR60.describe()

count    1412.000000
mean        7.292144
std         6.421018
min         0.000000
25%         3.213471
50%         6.245125
75%         9.956272
max        92.936803
Name: HR60, dtype: float64

Code

ax = south_gdf.plot(column='HR60')
ax.set_axis_off();

How many states are there in this dataset

Code

south_gdf.STATE_NAME.unique().shape

(17,)

How many counties?

Code

south_gdf.shape[0]

How many counties in each state?

Code

south_gdf.groupby(by='STATE_NAME').count()

	NAME	STATE_FIPS	CNTY_FIPS	FIPS	STFIPS	COFIPS	FIPSNO	SOUTH	HR60	HR70	...	BLK90	GI59	GI69	GI79	GI89	FH60	FH70	FH80	FH90	geometry
STATE_NAME
Alabama	67	67	67	67	67	67	67	67	67	67	...	67	67	67	67	67	67	67	67	67	67
Arkansas	75	75	75	75	75	75	75	75	75	75	...	75	75	75	75	75	75	75	75	75	75
Delaware	3	3	3	3	3	3	3	3	3	3	...	3	3	3	3	3	3	3	3	3	3
District of Columbia	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
Florida	67	67	67	67	67	67	67	67	67	67	...	67	67	67	67	67	67	67	67	67	67
Georgia	159	159	159	159	159	159	159	159	159	159	...	159	159	159	159	159	159	159	159	159	159
Kentucky	120	120	120	120	120	120	120	120	120	120	...	120	120	120	120	120	120	120	120	120	120
Louisiana	64	64	64	64	64	64	64	64	64	64	...	64	64	64	64	64	64	64	64	64	64
Maryland	24	24	24	24	24	24	24	24	24	24	...	24	24	24	24	24	24	24	24	24	24
Mississippi	82	82	82	82	82	82	82	82	82	82	...	82	82	82	82	82	82	82	82	82	82
North Carolina	100	100	100	100	100	100	100	100	100	100	...	100	100	100	100	100	100	100	100	100	100
Oklahoma	77	77	77	77	77	77	77	77	77	77	...	77	77	77	77	77	77	77	77	77	77
South Carolina	46	46	46	46	46	46	46	46	46	46	...	46	46	46	46	46	46	46	46	46	46
Tennessee	95	95	95	95	95	95	95	95	95	95	...	95	95	95	95	95	95	95	95	95	95
Texas	254	254	254	254	254	254	254	254	254	254	...	254	254	254	254	254	254	254	254	254	254
Virginia	123	123	123	123	123	123	123	123	123	123	...	123	123	123	123	123	123	123	123	123	123
West Virginia	55	55	55	55	55	55	55	55	55	55	...	55	55	55	55	55	55	55	55	55	55

17 rows × 69 columns

Which county had the highest median homicide rate in 1960?

Code

south_gdf[['STATE_NAME', 'HR60']].groupby(by='STATE_NAME').median()

	HR60
STATE_NAME
Alabama	9.623977
Arkansas	4.704111
Delaware	4.228385
District of Columbia	10.471807
Florida	9.970306
Georgia	9.300076
Kentucky	5.235436
Louisiana	6.840286
Maryland	5.335208
Mississippi	8.919274
North Carolina	7.633043
Oklahoma	4.269126
South Carolina	7.509437
Tennessee	4.877751
Texas	4.326215
Virginia	6.672004
West Virginia	2.623226

Which county had the highest maximum homicide rate in 1960?

Code

south_gdf[['STATE_NAME', 'HR60']].groupby(by='STATE_NAME').max()

	HR60
STATE_NAME
Alabama	24.903499
Arkansas	21.154427
Delaware	7.286472
District of Columbia	10.471807
Florida	40.744262
Georgia	53.304904
Kentucky	37.250885
Louisiana	18.243736
Maryland	14.327234
Mississippi	24.833923
North Carolina	25.660127
Oklahoma	17.088175
South Carolina	23.345940
Tennessee	20.894275
Texas	92.936803
Virginia	23.575639
West Virginia	11.482375

Intra-state dispersion

Code

south_gdf[['STATE_NAME', 'HR60']].groupby(by='STATE_NAME').std()

	HR60
STATE_NAME
Alabama	4.742337
Arkansas	4.574625
Delaware	1.815562
District of Columbia	NaN
Florida	7.990692
Georgia	7.906488
Kentucky	6.354316
Louisiana	4.189146
Maryland	4.064360
Mississippi	4.972698
North Carolina	4.596952
Oklahoma	4.231132
South Carolina	4.018644
Tennessee	4.354979
Texas	8.223844
Virginia	4.826707
West Virginia	2.773659

Code

sgdf = south_gdf[['STATE_NAME', 'HR60']].groupby(by='STATE_NAME').std()

Code

cv = sgdf / south_gdf[['STATE_NAME', 'HR60']].groupby(by='STATE_NAME').mean() * 100

Code

cv.sort_values(by='HR60', ascending=False)

	HR60
STATE_NAME
Texas	144.992919
Kentucky	96.815524
West Virginia	93.234007
Arkansas	81.223752
Oklahoma	81.114430
Tennessee	75.426226
Georgia	73.774440
Maryland	71.898559
Florida	68.252692
Virginia	66.924041
Louisiana	59.994571
Mississippi	57.457024
North Carolina	57.013871
Alabama	49.070812
South Carolina	48.083524
Delaware	34.966796
District of Columbia	NaN